About Me

I’m Rakib Al-Fahad, a Cloud Software Development Engineer at Intel Corporation, with a Ph.D. in Electrical and Computer Engineering from the University of Memphis. I am a multi-disciplinary researcher in the field of machine learning, exploratory data analysis, computer vision, cognitive science, and human-computer interaction. I specialize in providing research and analysis to support operations initiatives and strategic programs through descriptive, predictive, and prescriptive modeling, advanced statistical and complex mathematical techniques. I enjoy asking relevant research questions, connecting ideas, and am a lifelong learner.

CV / Resume:

For a detailed overview of my professional experience, education, and accomplishments, please download my Curriculum Vitae.

Education

  • Ph.D. in Electrical and Computer Engineering, University of Memphis (2016-2020)
    • Dissertation: “Multivariate Modeling of Cognitive Performance and Categorical Perception from Neuroimaging Data”
    • Advisor: Dr. Mohammed Yeasin
  • Master’s Degree in Electrical and Computer Engineering, University of Memphis (2013-2016)

Professional Experience

I am currently working as a Cloud Systems and Solutions Engineer at Intel Corporation (April 2024-Present), where I focus on optimizing cloud infrastructure, performance engineering, and system-level innovation.

Career Timeline

  • Cloud Systems and Solutions Engineer, Intel Corporation (April 2024-Present)
  • Cloud Solutions Engineer, Intel Corporation (June 2020-Present)
  • Machine Learning Intern, Intel Corporation (February 2019-December 2019)
  • Graduate Research Assistant, University of Memphis, CVPIA Lab (August 2013-April 2020)
  • Senior System Engineer, Grameenphone Ltd (August 2006-August 2013)

Cloud Solutions Engineer Responsibilities

  • Continuously learn and deep dive into partner technologies (e.g., Microsoft, VMware, RedHat) to solve customer problems
  • Design, execute, and analyze software and hardware architecture performance, capacity, and test results
  • Propose constant improvements to Reference Architecture in both software and hardware areas
  • Identify and propose solutions for hardware and software bottlenecks in Reference Architecture
  • Collaborate with other Intel departments and third-party vendors, acting as a bridge between teams to fix issues and determine best solutions

Key Accomplishments in Cloud Infrastructure

  • Executed multiple cloud infrastructure initiatives focused on performance optimization, workload automation, and system-level innovation aligned with organizational goals.
  • Contributed to the development and validation of memory-tiering strategies using Intel® In-Memory Analytics Accelerator (IAA), including benchmarking across Redis, Memcached, SPEC CPU 2017, and MGLRU workloads, Phoronix Test Suite benchmarking workloads.
  • Designed and implemented reproducible benchmarking environments and data pipelines to support performance analysis and technical documentation.
  • Developed infrastructure for automated reporting, visualization, and debugging to streamline performance evaluation in cloud-native environments.
  • Conducted comparative studies on memory management techniques such as Transparent Huge Pages (THP), Multi-size THP (mTHP), and compression algorithms (zstd, LZO), resulting in measurable improvements in memory efficiency and throughput.
  • Supported integration of IAA with Zswap in production cloud environments through kernel-level patching and delivery of technical documentation.
  • Incorporated customer feedback loops to drive continuous optimization and ensure alignment with real-world usage patterns.
  • Delivered outcomes that consistently met or exceeded key performance indicators, contributing to scalable, high-impact cloud infrastructure solutions.

Research Interests

As a multi-disciplinary researcher, my research interests include:

  • Machine learning and deep learning applications
  • Feature selection in higher dimensional data with limited sample size
  • Connectivity analysis, visualization, and graph mining
  • Bayesian nonparametric methods for clustering and time series analysis
  • Representations and visualization of visual concepts learned by convnets
  • Transfer learning and generative adversarial networks
  • Brain connectivity and neural dynamics
  • Cognitive science and human-computer interaction
  • Descriptive, predictive, and prescriptive modeling
  • Advanced statistical and complex mathematical modeling techniques
  • Machine learning applications in healthcare and neuroscience

Areas of Expertise

My expertise spans across multiple disciplines:

  • Exploratory Data Analysis: Visualization and pattern analysis
  • Graph Mining: Connectivity analysis and visualization
  • Bayesian Methods: Non-parametric methods for clustering and time series analysis
  • Feature Selection: Techniques for higher dimensional data with limited sample size
  • Machine Learning: Classical algorithms, clustering, and regression analysis
  • Deep Learning: CNNs, RNNs, transfer learning, and generative adversarial networks
  • Time Series Analysis: Prediction, classification, and forecasting using recurrent neural networks
  • Computer Vision: Representations and visualization of visual concepts learned by convnets
  • Big Data Analytics: Distributed computing systems using Scala and Apache Spark
  • Cloud Infrastructure: Performance optimization, workload automation, and system-level innovation
  • Neuroimaging Analysis: Processing and analyzing EEG, MRI, and fMRI data

Technical Skills

  • Programming Languages: Python, R, Scala, JavaScript
  • Libraries & Frameworks: TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy
  • Data Visualization: Matplotlib, Seaborn, Plotly, D3.js
  • Big Data Tools: Apache Spark, Hadoop
  • Database Systems: SQL, MongoDB, Neo4j
  • Cloud Platforms: AWS, Google Cloud Platform, Azure
  • Neuroimaging Tools: EEGLab, MNE-Python, SPM, FSL
  • Cloud Technologies: VMware, Microsoft Azure, RedHat

Notable Projects

Neural Dynamics Underlying Auditory Categorization

This project focused on understanding the neurobiology of normal perception of speech, music and auditory learning, with implications for interventions for communication problems that impair sound categorization. (Collaboration with PI: Gavin M. Bidelman, Co-I: M. Yeasin, UofM ECE - NIH-NIDCD R01)

Predictive Modeling of Cognitive Performance

Early Imaging-Based Predictive Modeling of Cognitive Performance Following Therapy for Childhood ALL: a collaboration project with St. Jude Children’s Research Hospital, Memphis, TN led by Dr. Mohammed Yeasin and Dr. Wilburn Reddick.

Human Connectome Project

Research on individual differences in human brain connectivity using deep learning and graph mining approaches, led by Dr. Mohammed Yeasin and Dr. Abbas Babajani-Feremi.

Epistemic State of Mind and Color of Emotion

Modeling epistemic state of mind and color of emotion from electroencephalogram (EEG) physiological data, as part of the blind ambition project led by Dr. Mohammed Yeasin.

Publications

  1. Al-Fahad, R., Yeasin, M., & Bidelman, G. M. (2020). Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions. Journal of Neural Engineering, 17(1), 016045.

  2. Mahmud, M. S., Ahmed, F., Al-Fahad, R., Moinuddin, K. A., Yeasin, M., Alain, C., & Bidelman, G. M. (2020). Decoding Hearing-Related Changes in Older Adults’ Spatiotemporal Neural Processing of Speech Using Machine Learning. Frontiers in Neuroscience, 14, 748.

  3. Al-Fahad, R., Yeasin, M., Glass, J. O., Conklin, H. M., Jacola, L. M., & Reddick, W. E. (2019). Early Imaging Based Predictive Modeling of Cognitive Performance Following Therapy for Childhood ALL. IEEE Access, 7, 146662-146674.

  4. Al-Fahad, R., & Yeasin, M. (2019). Micro-states based dynamic brain connectivity in understanding the commonality and differences in gender-specific emotion processing. International Joint Conference on Neural Networks (IJCNN), Budapest.

  5. Ahmed, F., Mahmud, M. S., Al-Fahad, R., Alam, S., & Yeasin, M. (2018). Image captioning for ambient awareness on a sidewalk. 1st International Conference on Data Intelligence and Security (ICDIS).

  6. Al-Fahad, R., Yeasin, M., Anam, A. S. M. I., & Elahian, B. (2017). Selection of stable features for modeling 4-D affective space from EEG recording. International Joint Conference on Neural Networks (IJCNN), 1202-1209.

  7. Al-Fahad, R., & Yeasin, M. (2016). Robust modeling of continuous 4-d affective space from EEG recording. 15th IEEE International Conference on Machine Learning and Applications.

  8. Moinuddin, K. A., Havugimana, F., Al-Fahad, R., Bidelman, G. M., & Yeasin, M. (2022). Unraveling spatial-spectral dynamics of speech categorization speed using convolutional neural networks. Brain Sciences, 13(1), 75.

  9. Al-Fahad, R., Yeasin, M., Moinuddin, K. A., & Bidelman, G. M. (2021). Micro-state-based neural decoding of speech categorization using Bayesian non-parametrics. bioRxiv, 2021.11.17.469011.

  10. Al-Fahad, R. (2020). Multivariate Modeling of Cognitive Performance and Categorical Perception from Neuroimaging Data. PhD Dissertation, The University of Memphis.

Contact Me

Feel free to reach out to me via LinkedIn, GitHub, or email at rakibalfahad@gmail.com. I’m always open to interesting projects, collaborations, or discussions about data science and machine learning.

Recent Work

Check out my blog, tutorials, and projects to see my recent work and publications.